Supervised Pattern Recognition Research Articles

Discrimination of vegetable oil types using Fourier transforms near infrared spectroscopy coupled with pattern recognition techniques

The aim of this research was to investigate the potential of near infrared spectroscopy (NIRs) coupled with pattern recognition techniques for discriminating of vegetable oil types (i.e. coconut oil, olive oil, rice bran oil, sesame oil, soybean oil and sun flower oil). Principle component analysis (PCA) was performed for clustering vegetable oil types. Five of supervised pattern recognition techniques such as soft independent modelling of class analogies (SIMCA), Partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (k-NN), support vector machine (SVM) and artificial neural network (ANN) were used to identify vegetable oil types. The PCA model could separate coconut oil from other vegetable oils. Two PLS-DA and SVM models showed 100% of precision, recall F-measure and accuracy for all vegetable oil whilst remainder techniques achieved a satisfactory classified performance. All supervised models could discriminate coconut oil from other oils with precision, recall F-Measure and accuracy of 100%. It seems that NIRs technique coupled with pattern recognition techniques is possible for discriminating vegetable oil types.

Methylation, sugar puckering and Z-form status of DNA from a heavy metal-acclimated freshwater Gordonia sp.

Heavy metal acclimation of bacteria is of particular interest in many aspects. It could add to our understanding of adaptation strategies applied by bacteria, as well as help us in devising ways to use such adaptive bacteria for bioremediation. In this study, we have explored the changes in the DNA of an aquatic Gordonia sp. acclimated to silver, cadmium, and lead. We have measured the changes in the DNA extracted from the acclimated bacteria by using ATR-FTIR coupled with unsupervised and supervised pattern recognition algorithms. Although whole-cell FTIR studies do reveal nucleic acid changes, the special care should be taken when considering marker nucleic acid bands in such spectra, as various other cell or tissue constituents also yield IR bands in the same region. An FTIR study on isolated DNA can be used to avoid this problem. The IR spectral profiles of the DNA molecules revealed significant changes in the backbone and sugar conformations of upon acclimation. We then further analyzed the DNA's global cytosine-methylation patterns of the heavy metal-acclimated bacteria. We aimed to find out whether epigenetic mechanisms operate in bacteria for survival and growth in inhibitory heavy metal concentrations or not. We found hypermethylation in Cd acclimation but hypomethylation for both Pb and Ag in Gordonia sp. Our results imply that changes in the conformational and methylation states of DNA seem to let bacteria to thrive in otherwise inhibitory conditions and mark the involvement of epigenetic modulation in acclimation processes.

Quality evaluation of traditional Chinese medicines based on fingerprinting.

The usage of traditional Chinese medicines has expanded globally, but the data about authentication, efficacy, and safety is far from sufficient to meet the criteria supporting their use worldwide due to complexity in the composition. Fingerprinting describes integral characterization and reflects interactive aspects of complex components; therefore, it can offer the possibility of evaluating quality of traditional Chinese medicines following the overall principle. Chemometric techniques introduce multivariate analytical methods into fingerprinting to obtain more information that is useful, which is consistent with the holistic thought and plays an important role in research on the substantial basis. In this review, we will start with three aspects to expound the quality evaluation of traditional Chinese medicines based on fingerprints. The analytical techniques used in developing fingerprints including chromatographic methods, spectroscopic methods, and capillary electrophoresis are introduced. Strategies for fingerprints analysis usually based on chemometric methods including unsupervised and supervised pattern recognition are described. Applications of fingerprints for multi-component quantification, quality control, screening of bioactive components, and fingerprint-efficacy relationship study are also outlined. Finally, we propose challenges and future perspectives of fingerprints in quality evaluation to promote the development of modernization and internationalization of traditional Chinese medicines.

Novel Biochemical Insights in the Cerebrospinal Fluid of Patients with Neurosyphilis Based on a Metabonomics Study.

Neurosyphilis is a chronic central nervous system infectious disease caused by Treponema pallidum. Our aim was to study the metabolic profiling in the cerebrospinal fluid of neurosyphilis patients and identify specific potential biomarkers. Fifteen cerebrospinal fluid samples from neurosyphilis patients and 14 non-neurosyphilis samples were analyzed by liquid chromatography-mass spectrometer (LC-MS). The LC-MS data were preprocessed by supervised pattern recognition to obtain diagnostic models. Both orthogonal projections to a latent structures discriminant analysis (OPLS-DA) and a t test were used to obtain specific metabolites for neurosyphilis. LC-MS data showed that the metabolites in cerebrospinal fluid (CSF) from neurosyphilis are different from the non-neurosyphilis group. The OPLS-DA model parameters R2Y and Q2Y are both more than 0.7 and indicated a satisfactory diagnostic performance. Bilirubin, L-histidine, prostaglandin E2, alpha-kamlolenic acid, and butyryl-L-carnitine and palmitoyl-L-carnitine were identified as novel potential biomarkers for neurosyphilis. The metabolic study of CSF may provide a new way to explore the pathogenesis of neurosyphilis.

Assessing Geographical Origin of Gentiana Rigescens Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics.

Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies (“low-level” and “mid-level”). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.

Vision‐based crater and rock detection using a cascade decision forest

Both crater and rock detection are components of the autonomous landing and hazard avoidance technology (ALHAT) sensor suite, as craters and rocks represent the majority of landing hazards. Furthermore, places with scientific values are very probable next to craters and rocks. Unsupervised approaches, which potentially use the pattern recognition techniques of ring threshold finding, perform quickly; however, they suffer from handling small craters. The supervised pattern recognition method is more powerful but is time‐consuming. To address these issues, here, a simultaneous multi‐size crater and rock detection algorithm is studied. The authors propose a new supervised machine‐learning framework using a cascade decision forest. Sliding windows are utilised in order to search basic features, and a multi‐grained cascade structure is introduced to enhance the framework's ability to learn the representations of the features. The training time of the proposed algorithm on a PC is comparable to that of deep neural networks, and the efficiency is enhanced for a large‐scale database. The outputs of the simulation verify the effectiveness and validity of the introduced technique.

Comparative Investigation for Rotten Xylem (kuqin) and Strip Types (tiaoqin) of Scutellaria baicalensis Georgi Based on Fingerprinting and Chemical Pattern Recognition

Scutellaria baicalensis Georgi (SBG) is not just as a traditional herbal medicine but also a popular functional food in China and other Asian countries. A sensitive simple strategy was developed for the first time to analyze SBG from eight different geographical sources using high-performance liquid chromatography (HPLC) coupled with multivariate chemometric methods. Two unsupervised pattern recognition models, hierarchical cluster analysis (HCA) and principal components analysis (PCA), and a supervised pattern recognition model, partial least squares discriminant analysis (PLS-DA), were used to analyze the chemical compositions and physical traits of SBG. The important chemical markers baicalin, baicalein, and wogonoside were analyzed quantitatively and with PLS-DA. These methods distinguished rotten xylem (kuqin) and strip types (tiaoqin) of SBG and found that the thickness of the slice had a significant impact on the classification of SBG. Two classes of strip types were identified: one as the uncut pharmaceutical, which was sectioned with a thickness >3 mm; the other as a thin-sectioned strip type, with a thickness of <2 mm. This fingerprinting technique coupled to a chemometric analysis was used for the simultaneous quantitation of three components (chemical markers) of SBG, and greatly simplified the complicated identification of the multiple components of this plant relative to traditional methods. The strategy can clearly distinguish between kuqin and tiaoqin of SBG, and suggests that the thickness of the slice can be used as the basis for evaluation of SBG. These data provide a theoretical basis and scientific evidence for the development and utilization of SBG.

K-CM application for supervised pattern recognition at Mt. Etna: an innovative tool to forecast flank eruptive activity

We investigated the relationship between the temporal monitoring series routinely recorded at Mt. Etna and the flank eruptions that occurred between January 2001 and April 2005 by the K-contractive map (K-CM) method pattern classifier with supervised learning. The reference dataset includes 28 variables and 1580 records collected over 52 months for a total of 301 eruptive days. A two-step analysis was performed. In the first step analysis, we used the 28 parameters of each day to recognize anomalies heralding a flank eruption. K-CM estimated a sensitivity higher than 95% and a specificity close to 100%. In the second step analysis, we considered each record comprising the 28 variables for 6 days as an input (for a total of 180 inputs) and the outcomes of the seventh day as an output to predict eruption or rest. In this case, K-CM showed sensitivity and specificity close to 98% and 100%, respectively. Results highlight the reliability of the K-CM method to build up a prediction algorithm able to alert the volcano experts a day before the occurrence of a potential flank eruption. The robustness of the two analyses was investigated by the behavior of the receiver operating characteristic curve. The relative area under the curve showed values close to 1, thus providing a valid measure of the performance of the classifier. Finally, a complete overview of the performance levels of the method used was explored analyzing the retrieved Molchan error diagram, in both cases, trajectories very close to the theoretical minimum.

Antiulcer activity of Cyperus alternifolius in relation to its UPLC-MS metabolite fingerprint: A mechanistic study

BackgroundGastric ulcer is one of the main prevalent gastrointestinal multi-etiological disorders with many associated complications and adverse effects. Our aim was to develop safer antiulcer therapies based on methanol or ethyl acetate extracts of tubers and aerial parts from Cyperus alternifolius. MethodsGastric ulceration was experimentally generated by administration of single oral doses of indomethacin (30 mg/kg) to fasted rats. The animals received methanol or ethyl acetate extracts of C. alternifolius tuber and methanol or ethyl acetate extracts of aerial parts at two dose levels (50 or 100 mg/kg). Ranitidine (50 mg/kg) was used as standard anti-ulcer drug. After 4 h, the ulcer number and the total ulcer score were determined and TNF-α was assessed. Also, pathological and histochemical examination for gastric mucosa were performed.The metabolome heterogeneity of the different extracts was explored using (UPLC-MS) aided by supervised pattern recognition, i.e., orthogonal partial least squares discriminate analysis (OPLS-DA). A second OPLS-DA model was employed to link the UPLC-MS derived metabolome of the different extracts to their antiulcer activity to identify activity mediating metabolites. ResultsThe extracts significantly reduced ulcer number, total ulcer score and TNF-α content in the stomach. Methanol or ethyl acetate extracts of tubers were most effective even more than ranitidine. In parallel, the histopathological examination showed an improvement of damaged mucosa. A high PAS reaction was observed in the treated groups indicating a relieve of the mucosal layer. A mechanistic clue of the C. alternifolius antiulcer potential was provided by the identification of its bioactive compounds using OPLS-DA. Both methanol extracts of tubers and aerial parts were more enriched in phenolic acids. The ethyl acetate extract of the aerial part was more abundant in two aldehydes. A mechanism of action was postulated based on their reported actions viz. α-carbonic anhydrase inhibition, anti-inflammatory and analgesic activity by its antioxidant activity and downregulation of several inflammatory mediators. ConclusionThis is the first study to report on the antiulcer activity of C. alternifolius tubers with identification of the key bioactive compounds and the mode of action. Future phytochemical and biological evaluation of the identified bioactive compounds are needed to confirm the plant tubers as safer alternative or adjunct therapy compared to conventional antiulcer drugs.

Identification of Cigarette Brands by Soft Independent Modeling of Class Analogy of Volatile Substances.

IntroductionThis study aimed to develop a method for discriminating cigarette brands based on the profiles of volatile components extracted from the tobacco fraction of the finished cigarettes to authenticate branded cigarettes of unknown origin.MethodsAn analytical method comprising direct thermal desorption coupled with gas chromatography-quadrupole time-of-flight mass spectrometry was developed for acquiring volatile profiles of cigarettes. About 290 samples of commercially available cigarettes were analyzed. Within this batch, 123 samples represented four popular cigarette brands. They were selected for in-depth characterization. Multivariate analysis was used to investigate the interrelations among volatile compounds of cigarettes and to identify characteristic markers for the cigarette discrimination. Supervised pattern recognition techniques were used for designing classification models.ResultsPrincipal component analysis covering all detected volatiles allowed the differentiation of cigarettes based on the brand. A number of 56 volatile components were identified as markers with high discrimination power. These compounds were used for establishing classification models. A method of soft independent modeling of class analogy developed for the four studied cigarette brands proved to be efficient in the classification of unknown cigarettes, with accuracy between 95.9% and 100%.ConclusionsThe data evaluation by soft independent modeling of class analogy was highly accurate in classification of unknown cigarettes with a low rate of false positives and false negatives. The developed models can be used for discrimination of genuine from non-genuine products with high level of probability.ImplicationsProfiling of volatiles, which is commonly used for authentication of different food commodities, was applied for the characterization of cigarette tobacco for the purpose of authentication a cigarette brand. Volatile components with a high discrimination power were identified by means of multivariate statistical methods and used for establishing of a classification model. The classification model was able to discriminate genuine from non-genuine cigarettes with a high level of prediction accuracy. This model could be a powerful tool for tobacco control to judge the authenticity of cigarettes.

Combination of vintage and new-fashioned analytical approaches for varietal and geographical traceability of olive oils

There is a great need for having accurate analytical methods able to guarantee the authenticity and traceability of foods, especially for those of high quality and economic value such as extra-virgin olive oil. In the present work, we assessed the potential of combining traditional analytical techniques, based on the characterization of the unsaponifiable fraction, together with novel nuclear magnetic resonance fingerprinting with the aim to investigate the effect of variety and geographical origin on olive oils collected from different locations across the province of Huelva (Spain). Various complementary supervised pattern recognition procedures and machine learning algorithms were then applied to build classification and predictive models. Extra-virgin olive oils were characterized by high concentrations of apparent β-sitosterol (93% of total sterol content), α-tocopherol (representing almost 91% of the total tocopherol fraction), squalene (90% of the total hydrocarbon content), heptacosanol and eicosane (the most abundant aliphatic alcohol and n-alkane, respectively). Furthermore, olive oil classes could be clearly differentiated on the basis of a characteristic chemical pattern, comprising tocopherols, squalene, sterols (campesterol, stigmasterol, β-sitosterol), aliphatic alcohols (heptacosanol, octacosanol) and some nuclear magnetic resonances related to fatty acid chains.

Rapid Recognition of Geoherbalism and Authenticity of a Chinese Herb by Data Fusion of Near-Infrared Spectroscopy (NIR) and Mid-Infrared (MIR) Spectroscopy Combined with Chemometrics

Fourier transform near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy play important roles in all fingerprint techniques because of their unique characteristics such as reliability, versatility, precision, and ease of measurement. In this paper, a supervised pattern recognition method based on the PLSDA algorithm by NIR and the NIR-MIR fusion spectra has been established to identify geoherbalism of Angelica dahurica from different regions and authenticity of Corydalis yanhusuo W. T. Wang. Comparing principle component analysis (PCA) cannot successfully identify geographical origins of Angelica dahurica. Linear discriminant analysis (LDA) also hardly distinguishes those origins. Furthermore, the PLSDA model based on the data fusion of NIR and IR was more accurate and efficient. But, the identification of authenticity of Corydalis yanhusuo W. T. Wang was still inaccurate in the PLSDA model. Consequently, data fusion of NIR-MIR original spectra combined with moving window partial least-squares discriminant analysis was firstly used and showed perfect properties on authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. It indicated that data fusion of NIR-MIR spectra combined with MWPLSDA could be considered as the promising tool for rapid discrimination of the geoherbalism and authenticity of more Chinese herbs in the future.

FEMa: a finite element machine for fast learning

Machine learning has played an essential role in the past decades and has been in lockstep with the main advances in computer technology. Given the massive amount of data generated daily, there is a need for even faster and more effective machine learning algorithms that can provide updated models for real-time applications and on-demand tools. This paper presents FEMa—a finite element machine classifier—for supervised learning problems, where each training sample is the center of a basis function, and the whole training set is modeled as a probabilistic manifold for classification purposes. FEMa has its theoretical basis in the finite element method, which is widely used for numeral analysis in engineering problems. It is shown FEMa is parameterless and has a quadratic complexity for both training and classification phases when basis functions are used that satisfy certain properties. The proposed classifier yields very competitive results when compared to some state-of-the-art supervised pattern recognition techniques.

Lithological classification of cement quarry using discriminant algorithms

As such in any industrial raw material site characterization study, making a lithological evaluation for cement raw materials includes a description of physical characteristics as well as grain size and chemical composition. For providing the cement components in accordance with the specifications required, making the classification of the cement raw material pit is needed. To make this identification in a spatial system at a quarry stage, the supervised pattern recognition analysis has been performed. By using four discriminant analysis algorithms, lithological classifications at three levels, which are with limestone, marly-limestone (calcareous marl) and marl, have been made based on the main chemical components such as calcium oxide (CaO), alumina (Al2O3), silica (SiO2), and iron (Fe2O3). The results show that discriminant algorithms can be used as strong classifiers in cement quarry identification. It has also recorded that the conditional and mixed classifiers perform better than the conventional discriminant algorithms.

Simultaneous Recognition of Species, Quality Grades, and Multivariate Calibration of Antioxidant Activities for 12 Famous Green Teas Using Mid- and Near-Infrared Spectroscopy Coupled with Chemometrics.

In this paper, mid- and near-infrared spectroscopy fingerprints were combined to simultaneously discriminate 12 famous green teas and quantitatively characterize their antioxidant activities using chemometrics. A supervised pattern recognition method based on partial least square discriminant analysis (PLSDA) was adopted to classify the 12 famous green teas with different species and quality grades, and then optimized sample-weighted least-squares support vector machine (OSWLS-SVM) based on particle swarm optimization was employed to investigate the quantitative relationship between their antioxidant activities and the spectral fingerprints. As a result, 12 famous green teas can be discriminated with a recognition rate of 100% by MIR or NIR data. However, compared with individual instrumental data, data fusion was more adequate for modeling the antioxidant activities of samples with RMSEP of 0.0065. Finally, the performance of the proposed method was evaluated and validated by some statistical parameters and the elliptical joint confidence region (EJCR) test. The results indicate that fusion of mid- and near-infrared spectroscopy suggests a new avenue to discriminate the species and grades of green teas. Moreover, the proposed method also implies other promising applications with more accurate multivariate calibration of antioxidant activities.

Classification of saline water for irrigated agriculture using near infrared spectroscopy coupled with pattern recognition techniques

This research aimed to create near infrared (NIR) spectroscopy models for the classification of saline water with a pattern recognition technique. A total of 112 water samples were collected from the Tha Chin river basin in Thailand. Water samples with salinity less than 0.2 g/l were identified as suitable for agriculture, while water samples with salinity higher than 0.2 g/l were found to be unsuitable. The NIR spectra of water samples were recorded using a Fourier transform (FT) NIR spectrometer in the wavenumber of 12,500–4,000 cm-1. The salinity of each water sample was analysed by electrical conductivity meter. Identification models were established with 5 supervised pattern recognition techniques including k-nearest neighbour (k-NN), support vector machine (SVM), artificial neural network (ANN), soft independent modelling of class analogies (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The performance of the NIR model was carried out with a split-test method. About 80% of spectra (90 spectra) were randomly selected to develop the classification models. After model development, the NIR spectroscopy models were used to classify the categories of the remaining samples (22 samples). The ANN model showed the highest performance for classifying saline water with precision, recall, F-measure and accuracy of 84.6%, 100.0%, 91.7% and 90.9%, respectively. Other techniques presented satisfactory classification results with accuracy greater than 68.2%. This point indicated that NIR spectroscopy coupled with the pattern recognition technique could be applied to classify saline water for agricultural use according to salinity level in natural resources.

Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

Tracing the Geographical Origin of Lentils (Lens culinaris Medik.) by Infrared Spectroscopy and Chemometrics

The feasibility of applying the infrared spectroscopy for the geographical origin traceability of lentils from two different countries (Italy and Canada) was investigated. In particular, lentil samples were analyzed by Fourier transform near- and mid-infrared (FT-NIR and FT-MIR) spectroscopy and then discriminated by applying supervised models, i.e., linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA). To avoid LDA overfitting, two variable strategies were adopted, i.e., a variable reduction by principal component analysis and a variable compression by wavelet packet transform algorithm. FT-MIR models were more discriminating compared to FT-NIR ones with prediction abilities ranging from 98 to 100% and from 91 to 100% for cross- and external validation, respectively. The combination of the FT-MIR and FT-NIR data did not improve the model performances. These findings demonstrated the suitability of the FT-MIR spectroscopy, in combination with supervised pattern recognition techniques, to successfully classify lentils according to their geographical origin.

Analysis of Forming Limits in Sheet Metal Forming with Pattern Recognition Methods. Part 2: Unsupervised Methodology and Application.

The forming limit curve (FLC) is used in finite element analysis (FEA) for the modeling of onset of sheet metal instability during forming. The FLC is usually evaluated by achieving forming measurements with optical measurement system during Nakajima tests. Current evaluation methods such as the standard method according to DIN EN ISO 12004-2 and time-dependent methods limit the evaluation range to a fraction of the available information and show weaknesses in the context of brittle materials that do not have a pronounced constriction phase. In order to meet these challenges, a supervised pattern recognition method was proposed, whose results depend on the quality of the expert annotations. In order to alleviate this dependence on experts, this study proposes an unsupervised classification approach that does not require expert annotations and allows a probabilistic evaluation of the onset of localized necking. For this purpose, the results of the Nakajima tests are examined with an optical measuring system and evaluated using an unsupervised classification method. In order to assess the quality of the results, a comparison is made with the time-dependent method proposed by Volk and Hora, as well as expert annotations, while validated with metallographic investigations. Two evaluation methods are presented, the deterministic FLC, which provides a lower and upper limit for the onset of necking, and a probabilistic FLC, which allows definition of failure quantiles. Both methods provide a necking range that shows good correlation with the expert opinion as well as the results of the time-dependent method and metallographic examinations.

Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint⁻Chemical Pattern Recognition.

Phellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid chromatography, and multivariate chemometric methods were used for comprehensive analysis. Two unsupervised pattern recognition models (principal component analysis and hierarchical cluster analysis) and a supervised pattern recognition model (partial least squares discriminant analysis) were established on the basis of the chemical composition and physical traits of PCC and PAC. PCC and PAC were found to be distinguishable by these methods. The PCC category was divisible into two categories, one with more crude cork and a maximum thickness of ~1.5 mm, and the other with less net crude cork and a maximum thickness of 0.5 mm. According to the model established by partial least squares discriminant analysis (PLS-DA), the important chemical marker berberine hydrochloride was obtained and analyzed quantitatively. From these results combined with chemometric and content analyses, the preliminary classification standards for phellodendron were established as three grades: superior, first-order and mixed. Compared with the traditional identification methods of thin layer chromatography identification and microscopic identification, our method for quality evaluation is relatively simple. It provides a basis and reference for identification of PCC and enables establishment of grade standards. It also could be applied in quality control for compound preparations containing PCC.